Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Blockade of TGF-ß (Transforming Growth Factor Beta) Signaling by Deletion of Tgfbr2 in Smooth Muscle Cells of 11-Month-Old Mice Alters Aortic Structure and Causes Vasomotor Dysfunction-Brief Report.

BACKGROUND: To test the hypothesis that smooth muscle cell (SMC) TGF-ß (transforming growth factor beta) signaling contributes to maintenance of aortic structure and function beyond the early postnatal period. METHODS: We deleted the TBR2 (type 2 TGF-ß receptor) in SMC of 11-month-old mice (genotype Acta2-CreERT2+/0Tgfbr2f/f, termed TBR2SMΔ) and compared their ascending aorta structure and vasomotor function to controls (Acta2-CreERT20/0Tgfbr2f/f, termed TBR2f/f). RESULTS: We confirmed loss of aortic SMC TBR2 by immunoblotting. Four weeks after SMC TBR2 loss, TBR2SMΔ mice did not have aortic rupture, ulceration, dissection, dilation, or evidence of medial hemorrhage. However, aortic medial area of TBR2SMΔ mice was increased by 27% (0.14±0.01 versus 0.11±0.01 mm2; P=0.01) and medial thickness was increased by 23% (40±1.9 versus 33±1.3 µm; P=0.004) compared with littermate controls. Wire myography performed on ascending aortic rings showed hypercontractility of TBR2SMΔ aortas to phenylephrine (Emax, 15.9±1.2 versus 10.8±0.7 mN; P=0.0003) and reduced relaxation and sensitivity to acetylcholine (Emax, 64±14% versus 96±2%; P=0.001; -logEC50, 6.9±0.1 versus 7.7±0.1; P=0.0001). Neither maximal relaxation nor sensitivity to sodium nitroprusside differed (Emax, 102±0.3% versus 101±0.3%; -logEC50, 8.0±0.04 versus 7.9±0.08; P>0.4 for both). CONCLUSIONS: Loss of TGF-ß signaling in aortic SMC of 1-year-old mice does not cause early severe aortopathy or death; however, it causes mild structural and substantial physiological abnormalities. SMC TGF-ß signaling plays an important role in maintaining aortic homeostasis in older mice. This role should be considered in the design of clinical studies that aim to prevent aortopathy by blocking SMC TGF-ß signaling.

Sex differences in predictors and regional patterns of brain age gap estimates.

The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.

Functional Delineation of Prefrontal Networks Underlying Working Memory in Schizophrenia: A Cross-data-set Examination.

BACKGROUND: Working memory (WM) impairment in schizophrenia substantially impacts functional outcome. Although the dorsolateral pFC has been implicated in such impairment, a more comprehensive examination of brain networks comprising pFC is warranted. The present research used a whole-brain, multi-experiment analysis to delineate task-related networks comprising pFC. Activity was examined in schizophrenia patients across a variety of cognitive demands. METHODS: One hundred schizophrenia patients and 102 healthy controls completed one of four fMRI tasks: a Sternberg verbal WM task, a visuospatial WM task, a Stroop set-switching task, and a thought generation task (TGT). Task-related networks were identified using multi-experiment constrained PCA for fMRI. Effects of task conditions and group differences were examined using mixed-model ANOVA on the task-related time series. Correlations between task performance and network engagement were also performed. RESULTS: Four spatially and temporally distinct networks with pFC activation emerged and were postulated to subserve (1) internal attention, (2) auditory-motor attention, (3) motor responses, and (4) task energizing. The "energizing" network-engaged during WM encoding and diminished in patients-exhibited consistent trend relationships with WM capacity across different data sets. The dorsolateral-prefrontal-cortex-dominated "internal attention" network exhibited some evidence of hypoactivity in patients, but was not correlated with WM performance. CONCLUSIONS: Multi-experiment analysis allowed delineation of task-related, pFC-anchored networks across different cognitive constructs. Given the results with respect to the early-responding "energizing" network, WM deficits in schizophrenia may arise from disruption in the "energization" process described by Donald Stuss' model of pFC functions.

Functional brain networks involved in lexical decision.

Functional magnetic resonance imaging (fMRI)5 studies on lexical decision (LD)6 attempting to isolate the brain network underlying access to lexical representations can be confounded by attentional and response processes. However, manipulating the "wordlikeness" of the LD stimuli can facilitate functional interpretation of each emerging brain network, providing principles for separation of attentional demand from linguistic processing. This is because activation of difficult-to-access lexical representations (for obscure real words), and avoidance of interfering word properties (for wordlike non-words), are both generally attentionally demanding. Therefore, congruent patterns of activation would be predicted for general-attention-responsive networks, but opposing patterns for language-responsive networks. 59 healthy adults performed a LD task, and multidimensional functional connectivity analysis was used to extract three functional brain networks. A linguistic processing network (LPN) was separated from attention/response networks anatomically (LPN included Broca's and Wernicke's areas), but also temporally by showing reduced activation for the most attentionally demanding condition (i.e., wordlike non-words). This demonstrated that during LD in fMRI a network involved in linguistic processing can be disentangled from attention- and response-specific networks, using a combination of experimental design and multidimensional analysis methods.

Symptom-related attributional biases in schizophrenia and bipolar disorder.

INTRODUCTION: Biases in causal attributions and evidence integration have been implicated in delusions, but have not been investigated simultaneously to examine additive or multiplicative effects. It was hypothesised that paranoid delusions would correlate with self-serving and personalising biases ("defence" model of paranoia), particularly when these biases were disconfirmed. METHODS: Constrained principal component analysis was used to investigate differences between schizophrenia patients (paranoid vs. non-paranoid), bipolar disorder patients, and healthy controls, as well as to examine the extent to which psychotic symptoms could predict patterns of responding on a novel attributional bias task (Attributional Style BADE, or ASB) that requires integrating contextual information. RESULTS: Although no group differences were found, disorganisation and manic symptoms correlated with situation attributions and self-blame when such attributions were unsupported by the available evidence, and depression and anxiety correlated with other-person and self attributions (not situation attributions) when confirmed by the available evidence, regardless of diagnosis. CONCLUSIONS: While group differences accounted for little variance in responses on the ASB task, a transdiagnostic association between symptoms of psychosis and the ASB task was observed. This highlights the importance of considering symptom profiles rather than diagnostic groupings when investigating cognitive biases and related non-pharmacological treatments.

Reduced functional connectivity during controlled semantic integration in schizophrenia: A multivariate approach.

Impairment in controlled semantic association is a central feature of schizophrenia, and the goal of the current functional magnetic resonance imaging study was to identify the neural correlates of this impairment. Thirty people with schizophrenia and 30 healthy age- and gender-matched control subjects performed a task requiring participants to match word pairs that varied in semantic distance (distant vs. close). A whole-brain multivariate connectivity analysis revealed three functional brain networks of primary interest engaged by the task: two configurations of a multiple demands network, in which brain activity did not differ between groups, and a semantic integration network, in which coordinated activity was reduced in schizophrenia patients relative to healthy controls, for distantly relative to closely related word pairs. The hypoactivity during controlled semantic integration in schizophrenia reported here, combined with hyperactivity in automatic semantic association reported in the literature, suggests an imbalance between controlled integration and automatic association. This provides a biological basis for Bleuler's concept of schizophrenia as a "split mind" arising from an impaired ability to form coherent associations between semantic concepts.

Hypoactivation of the language network during auditory imagery contributes to hallucinations in Schizophrenia.

Auditory verbal hallucinations (AVHs) involve perceptions, often voices, in the absence of external stimuli, and rank among the most common symptoms of schizophrenia. Metrical stress evaluation requires determination of the stronger syllable in words, and therefore requires auditory imagery, of interest for investigation of hallucinations in schizophrenia. The current functional magnetic resonance imaging study provides an updated whole-brain network analysis of a previously published study on metrical stress, which showed reduced directed connections between Broca's and Wernicke's regions of interest (ROIs) for hallucinations. Three functional brain networks were extracted, with the language network (LN) showing an earlier and shallower blood-oxygen-level dependent (BOLD) response for hallucinating patients, in the auditory imagery condition only (the reduced activation for hallucinations observed in the original ROI-based results were not specific to the imagery condition). This suggests that hypoactivation of the LN during internal auditory imagery may contribute to the propensity to hallucinate. This accords with cognitive accounts holding that an impaired balance between internal and external linguistic processes (underactivity in networks involved in internal auditory imagery and overactivity in networks involved in speech perception) contributes to our understanding of the biological underpinnings of hallucinations.

Apolipoprotein A-I vascular gene therapy reduces vein-graft atherosclerosis.

Coronary artery venous bypass grafts typically fail because of atherosclerosis driven by lipid and macrophage accumulation. Therapy for vein-graft atherosclerosis is limited to statin drugs, which are only modestly effective. We hypothesized that transduction of vein-graft endothelium of fat-fed rabbits with a helper-dependent adenovirus expressing apolipoprotein AI (HDAdApoAI) would reduce lipid and macrophage accumulation. Fat-fed rabbits received bilateral external jugular vein-to-carotid artery interposition grafts. Four weeks later, one graft per rabbit (n = 23 rabbits) was infused with HDAdApoAI and the contralateral graft with HDAdNull. Grafts were harvested 12 weeks later. Paired analyses of grafts were performed, with vein graft cholesterol, intimal lipid, and macrophage content as the primary endpoints. HDAd genomes were detected in all grafts. APOAI mRNA was median 63-fold higher in HDAdApoAI grafts versus HDAdNull grafts (p < 0.001). HDAdApoAI grafts had a mean 15% lower total cholesterol (by mass spectrometry; p = 0.003); mean 19% lower intimal lipid (by oil red O staining; p = 0.02); and mean 13% lower expression of the macrophage marker CD68 (by reverse transcriptase-mediated quantitative PCR; p = 0.008). In vivo transduction of vein-graft endothelium achieves persistent APOAI expression and reduces vein-graft cholesterol, intimal lipid, and CD68 expression. Vascular gene therapy with APOAI has promise for preventing vein-graft failure caused by atherosclerosis.

Accelerated Global and Local Brain Aging Differentiate Cognitively Impaired From Cognitively Spared Patients With Schizophrenia.

Background: Accelerated aging has been proposed as a mechanism underlying the clinical and cognitive presentation of schizophrenia. The current study extends the field by examining both global and regional patterns of brain aging in schizophrenia, as inferred from brain structural data, and their association with cognitive and psychotic symptoms. Methods: Global and local brain-age-gap-estimates (G-brainAGE and L-brainAGE) were computed using a U-Net Model from T1-weighted structural neuroimaging data from 84 patients (aged 16-35 years) with early-stage schizophrenia (illness duration <5 years) and 1,169 healthy individuals (aged 16-37 years). Multidomain cognitive data from the patient sample were submitted to Heterogeneity through Discriminative Analysis (HYDRA) to identify cognitive clusters. Results: HYDRA classified patients into a cognitively impaired cluster (n = 69) and a cognitively spared cluster (n = 15). Compared to healthy individuals, G-brainAGE was significantly higher in the cognitively impaired cluster (+11.08 years) who also showed widespread elevation in L-brainAGE, with the highest deviance observed in frontal and temporal regions. The cognitively spared cluster showed a moderate increase in G-brainAGE (+8.94 years), and higher L-brainAGE localized in the anterior cingulate cortex. Psychotic symptom severity in both clusters showed a positive but non-significant association with G-brainAGE. Discussion: Accelerated aging in schizophrenia can be detected at the early disease stages and appears more closely associated with cognitive dysfunction rather than clinical symptoms. Future studies replicating our findings in multi-site cohorts with larger numbers of participants are warranted.

Multiple Functional Brain Networks Related to Pain Perception Revealed by fMRI.

The rise of functional magnetic resonance imaging (fMRI) has led to a deeper understanding of cortical processing of pain. Central to these advances has been the identification and analysis of "functional networks", often derived from groups of pre-selected pain regions. In this study our main objective was to identify functional brain networks related to pain perception by examining whole-brain activation, avoiding the need for a priori selection of regions. We applied a data-driven technique-Constrained Principal Component Analysis for fMRI (fMRI-CPCA)-that identifies networks without assuming their anatomical or temporal properties. Open-source fMRI data collected during a thermal pain task (33 healthy participants) were subjected to fMRI-CPCA for network extraction, and networks were associated with pain perception by modelling subjective pain ratings as a function of network activation intensities. Three functional networks emerged: a sensorimotor response network, a salience-mediated attention network, and the default-mode network. Together, these networks constituted a brain state that explained variability in pain perception, both within and between individuals, demonstrating the potential of data-driven, whole-brain functional network techniques for the analysis of pain imaging data.

Functional brain networks underlying probabilistic reasoning and delusions in schizophrenia.

Delusions in schizophrenia are false beliefs that are assigned certainty and not afforded the scrutiny that normally gives rise to doubt, even under conditions of weak evidence. The goal of the current functional magnetic resonance imaging (fMRI) study is to identify the brain network(s) involved in gathering information under conditions of weak evidence, in people with schizophrenia experiencing delusions. fMRI activity during probabilistic reasoning in people with schizophrenia experiencing delusions (n = 29) compared to people with schizophrenia not experiencing delusions (n = 41) and healthy controls (n = 41) was observed when participants made judgments based on evidence that weakly or strongly matched (or mismatched) with the focal hypothesis. A brain network involved in visual attention was strongly elicited for conditions of weak evidence for healthy controls and patients not experiencing delusions, but this increase was absent for patients experiencing delusions. This suggests that the state associated with delusions manifests in fMRI as reduced activity in an early visual attentional process whereby weak evidence is incorrectly stamped as conclusive, manifestating as a feeling of fluency and misplaced certainty, short-circuiting the search for evidence, and providing a candidate neural process for 'seeding' delusions.

Task-merging for finer separation of functional brain networks in working memory.

BACKGROUND: In task-state functional magnetic resonance imaging (fMRI), hemodynamic response (HDR) shapes help identify cognitive process(es) supported by a brain network. However, when distinguishable networks have similar time courses, the low temporal resolution of the HDRs may result in spatial and temporal blurring of these networks. The present study demonstrated how task-merging and multivariate analysis allows data-driven separation of working memory (WM) processes. This was achieved by combining a WM task with the Thought Generation Task (TGT), a task which also requires attention to internal representations but no overt behavioral response. METHODS: 69 adults completed one of two tasks: (1) a Sternberg WM task, whereby participants had to remember a string of letters over a 4-sec delay or no delay, and (2) the TGT task, whereby participants internally generated or listened to a function of an object. WM data were analyzed in isolation and then with the TGT data, using multi-experiment constrained principal component analysis for fMRI (fMRI-CPCA). The function of each network was interpreted by evaluating HDR shapes across conditions (within and between tasks). RESULTS: The multi-experiment analysis produced three WM networks involving frontoparietal connectivity; two of these were combined when the WM task was analyzed alone. Notably, one network exhibited HDRs consistent with volitional attention to internal representations in both tasks (i.e., strongest in WM trials with a maintenance phase and in TGT trials involving silent thought). This network was separated from visual attention and motor response networks in the multi-experiment analysis only. CONCLUSIONS: Task-merging and multivariate analysis allowed us to differentiate WM networks possibly underlying internal attention (maintenance), visual attention (encoding), and response processes. Further, it allowed postulation of the cognitive operations subserved by each network by providing HDR shapes. This approach facilitates characterization of network functions by allowing direct comparisons of activity across different cognitive domains.

Task-Related Functional Connectivity Analysis of Emotion Discrimination in a Family Study of Schizophrenia.

Poor emotion recognition is a core deficit in schizophrenia and is associated with poor functional outcome. Functional magnetic resonance imaging (fMRI) multivariate analysis methods were used to elucidate the neural underpinnings of face and emotion processing associated with both genetic liability and disease-specific effects. Schizophrenia patients, relatives, and controls completed a task that included 4 facial emotion discrimination conditions and an age discrimination condition during fMRI. Three functional networks were derived from the data: the first involved in visual attention and response generation, the second a default mode network (DMN), and a third involved in face and emotion processing. No differences in activation were found between groups for the visual attention and response generation network, suggesting that basic processes were intact. Both schizophrenia patients and relatives showed evidence for hyperdeactivation in the DMN compared to controls, with relatives being intermediate, suggesting a genetic liability effect. Both disease-specific and genetic liability effects were found for the face processing network, which included the amygdala. Patients exhibited lower coordinated network activity compared to controls and relatives across all facial discrimination conditions. Additionally, in relation to the other emotion discrimination conditions, a heightened coordinated response during fear and anger discrimination was observed in schizophrenia compared to other conditions, whereas relatives demonstrated heightened coordinated activity for anger discrimination only relative to other emotion conditions. With regards to brain functioning, this study found that schizophrenia is associated with abnormal processing of threat-related information, and that in part may be associated with the genetic risk for the disorder, suggesting that the facial and emotion processing network could be targeted for intervention.

Altered balance of functional brain networks in Schizophrenia.

Activity in dorsal attention (DAN) and frontoparietal (FPN) functional brain networks is linked to allocation of attention to external stimuli, and activity in the default-mode network (DMN) is linked to allocation of attention to internal representations. Tasks requiring attention to external stimuli shift activity to the DAN/FPN and away from the DMN, and optimal task performance depends on balancing DAN/FPN against DMN activity. The current functional magnetic resonance imaging (fMRI) study assessed the balance of DAN/FPN and DMN activity in 13 schizophrenia patients and 13 healthy controls while they were engaged in a task switching Stroop paradigm which demanded internally directed attention to task instructions. The typical pattern of reciprocity between the DAN/FPN and DMN was observed for healthy controls but not for patients, suggesting a reduction in the internally focussed thought important for maintenance of instructions and strategies in schizophrenia. The observed alteration in the balance between DAN/FPN and DMN in patients may reflect a general mechanism underlying multiple forms of cognitive impairment in schizophrenia, including global processing deficits such as cognitive inefficiency and impaired context processing.